Graph Learning Techniques

This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. A valuable reference for advance undergraduate and postgraduate students in Network Analysis, Privacy and Security in Data Analytics, Graph Theory, and Applications in Healthcare.

Autorentext

Baoling Shan is currently a Lecturer at University of Science and Technology Beijing, Beijing, China.

Xin Yuan

Wei Ni is a Principal Research Scientist at CSIRO, Sydney, Australia, a Fellow of IEEE, a Conjoint Professor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University.

Ren Ping Liu is a Professor and the Head of the Discipline of Network and Cybersecurity, University of Technology Sydney (UTS), Ultimo, NSW, Australia.

Eryk Dutkiewicz is currently the Head of School of Electrical and Data Engineering at the University of Technology Sydney, Australia. He is a Senior Member of IEEE and his research interests cover 5G/6G and IoT networks.

Inhalt

Table of Contents

Abstract

List of Figures

List of Tables

Contributors

1. Introduction

2. Privacy Considerations in Graph and Graph Learning

3. Existing Technologies of Graph Learning

4. Graph Extraction and Topology Learning of Band-limited Signals

5. Graph Learning from Band-Limited Data by Graph Fourier Transform Analysis

6. Graph Topology Learning of Brain Signals

7. Graph Topology Learning of COVID-19

8. Preserving the Privacy of Latent Information for Graph-Structured Data

9. Future Directions and Challenges

10. Appendix

Bibliography

Index